1. Field of the Invention
The present invention relates to apparatuses and methods for the automatic evaluation of the perceived video quality.
2. Description of the Related Art
Deciding on the perceptual image quality for video sequences automatically is of great importance for quality-of-service (QoS) distribution, broadcasting and for consumer-electronics manufacturers.
Conventionally, perceived video quality is assessed subjectively. Although expert viewers may notice imperfections in quality, such as artifacts, the general public often does not. Accordingly, as the general public is the majority of purchasers of consumer-electronics, the manufacturers, broadcasters and distributors continually strive to appeal to this group in terms of quality.
Subject assessment of video quality is a time consuming process with inconsistent results at best. Panels of viewers will rate the same video sequences differently. In fact, the same panel of viewers may rate the same video sequence differently each time. Thus, pure subjective assessment of video quality requires statistical analysis in an attempt to remove ambiguities of subjective assessment.
Accordingly, objective evaluation methods are preferred because of their consistent results. Such evaluation methods are automated to quickly evaluate video quality and to quantify the merit of the video quality. Of course, there must be a correlation of the objective methods with predetermined subjective standards of quality because it is the viewer who will ultimately judge quality according to subjective terms.
Objective evaluation methods utilize metrics to quantify video quality. Metrics are sets of measurements, which in a video sense, comprise a set of automated parameters for a measurement of a certain objective or objectives. For example, there can be metrics for measuring distortion, artifacts of images, artifacts near edges of images, color perception, contrast sensitivity, spatial and temporal channels, just to name a few.
The final determinant for the quality of these automatic video-quality measuring metrics is its degree of correlation with subjective evaluation; the higher the correlation, the better the metric.
Different objective video quality metrics have been proposed, which vary widely according to:                Performance regarding how much they correlate with subjective quality assessment results;        Stability, in that some models excel when certain kinds of artifacts are encountered (e.g. blocking, corner artifacts in MPEG decoding), but they degrade significantly when applied to other kinds of artifacts; and        Complexity, wherein a number of models rely on complicated human vision system (HVS) simulation, which required a lot of computation power, whereas other models rely on very simple calculations (e.g. signal to noise ratio).        
Obviously, relying on a single metric would restrict the evaluation to the advantages and disadvantages of the particular single metric.
Accordingly, there is a need to use a different objective video quality metrics instead of a single one. Previously, a linear combination of objective video quality metrics has been used to mimic the subjective evaluation of video quality. Such a linear combination assumes that the different metrics are independent of each other, and consequently could be fused by a linear model.